
from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
	from transformers import (
	    TFAutoModel,
	    TFAutoModelForCausalLM,
	    TFAutoModelForMaskedLM,
	    TFAutoModelForPreTraining,
	    TFAutoModelForQuestionAnswering,
	    TFAutoModelForSeqaSeqLM,
	    TFAutoModelForSequenceClassification,
	    TFAutoModelForTableQuestionAnswering,
	    TFAutoModelForTokenClassification,
	    TFAutoModelWithLMHead,
	    TFBertForMaskedLM,
	    TFBertForPreTraining,
	    TFBertForQuestionAnswering,
	    TFBertForSequenceClassification,
	    TFBertModel,
	    TFFunnelBaseModel,
	    TFFunnelModel,
	    TFGPTaLMHeadModel,
	    TFRobertaForMaskedLM,
	    TFTaForConditionalGeneration,
	    TFTapasForQuestionAnswering,
	)
	from transformers.models.auto.modeling_tf_auto import (
	    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_MASKED_LM_MAPPING,
	    TF_MODEL_FOR_PRETRAINING_MAPPING,
	    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
	    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	    TF_MODEL_MAPPING,
	)
	from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
	from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST







class 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	Dict									=			"""new-model"""


if is_tf_available():







	class 			A_  (     __lowerCamelCase  ):




		'''simple docstring'''


		_UpperCamelCase	:	Tuple									=			NewModelConfig







@require_tf
class 			A_  (     unittest.TestCase  ):




	'''simple docstring'''


	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'bert-base-cased'
		lowercase	    =       AutoConfig.from_pretrained(snake_case			)
		self.assertIsNotNone(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)

		lowercase	    =       TFAutoModel.from_pretrained(snake_case			)
		self.assertIsNotNone(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       'bert-base-cased'
		lowercase	    =       AutoConfig.from_pretrained(snake_case			)
		self.assertIsNotNone(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)

		lowercase	    =       TFAutoModelForPreTraining.from_pretrained(snake_case			)
		self.assertIsNotNone(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelForCausalLM.from_pretrained(snake_case			)
			lowercase    ,   lowercase	    =       TFAutoModelForCausalLM.from_pretrained(snake_case						,       output_loading_info=snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelWithLMHead.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelForMaskedLM.from_pretrained(snake_case			)
			lowercase    ,   lowercase	    =       TFAutoModelForMaskedLM.from_pretrained(snake_case						,       output_loading_info=snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelForSeqaSeqLM.from_pretrained(snake_case			)
			lowercase    ,   lowercase	    =       TFAutoModelForSeqaSeqLM.from_pretrained(snake_case						,       output_loading_info=snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
		for model_name in ["bert-base-uncased"]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelForSequenceClassification.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	def       SCREAMING_SNAKE_CASE__		(			self			):
		# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
		for model_name in ["bert-base-uncased"]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelForQuestionAnswering.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	@slow
	@require_tensorflow_probability
	def       SCREAMING_SNAKE_CASE__		(			self			):
		for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
			lowercase	    =       AutoConfig.from_pretrained(snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)

			lowercase	    =       TFAutoModelForTableQuestionAnswering.from_pretrained(snake_case			)
			lowercase    ,   lowercase	    =       TFAutoModelForTableQuestionAnswering.from_pretrained(
			    snake_case						,       output_loading_info=snake_case			)
			self.assertIsNotNone(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       TFAutoModelWithLMHead.from_pretrained(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)
		self.assertEqual(model.num_parameters()						,       1_4410			)
		self.assertEqual(model.num_parameters(only_trainable=snake_case			)						,       1_4410			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       TFAutoModelWithLMHead.from_pretrained(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)
		self.assertEqual(model.num_parameters()						,       1_4410			)
		self.assertEqual(model.num_parameters(only_trainable=snake_case			)						,       1_4410			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
		lowercase	    =       TFAutoModel.from_pretrained('sgugger/funnel-random-tiny'			)
		self.assertIsInstance(snake_case						,       snake_case			)

		lowercase	    =       copy.deepcopy(model.config			)
		lowercase	    =       ['FunnelBaseModel']
		lowercase	    =       TFAutoModel.from_config(snake_case			)
		self.assertIsInstance(snake_case						,       snake_case			)

		with tempfile.TemporaryDirectory() as tmp_dir:
			model.save_pretrained(snake_case			)
			lowercase	    =       TFAutoModel.from_pretrained(snake_case			)
			self.assertIsInstance(snake_case						,       snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		try:
			AutoConfig.register('new-model'						,       snake_case			)

			lowercase	    =       [
			    TFAutoModel,
			    TFAutoModelForCausalLM,
			    TFAutoModelForMaskedLM,
			    TFAutoModelForPreTraining,
			    TFAutoModelForQuestionAnswering,
			    TFAutoModelForSequenceClassification,
			    TFAutoModelForTokenClassification,
			]

			for auto_class in auto_classes:
				with self.subTest(auto_class.__name__			):
					# Wrong config class will raise an error
					with self.assertRaises(snake_case			):
						auto_class.register(snake_case						,       snake_case			)
					auto_class.register(snake_case						,       snake_case			)
					# Trying to register something existing in the Transformers library will raise an error
					with self.assertRaises(snake_case			):
						auto_class.register(snake_case						,       snake_case			)

					# Now that the config is registered, it can be used as any other config with the auto-API
					lowercase	    =       BertModelTester(self			).get_config()
					lowercase	    =       NewModelConfig(**tiny_config.to_dict()			)
					lowercase	    =       auto_class.from_config(snake_case			)
					self.assertIsInstance(snake_case						,       snake_case			)

					with tempfile.TemporaryDirectory() as tmp_dir:
						model.save_pretrained(snake_case			)
						lowercase	    =       auto_class.from_pretrained(snake_case			)
						self.assertIsInstance(snake_case						,       snake_case			)

		finally:
			if "new-model" in CONFIG_MAPPING._extra_content:
				del CONFIG_MAPPING._extra_content["new-model"]
			for mapping in (
			    TF_MODEL_MAPPING,
			    TF_MODEL_FOR_PRETRAINING_MAPPING,
			    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
			    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
			    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
			    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
			    TF_MODEL_FOR_MASKED_LM_MAPPING,
			):
				if NewModelConfig in mapping._extra_content:
					del mapping._extra_content[NewModelConfig]





	def       SCREAMING_SNAKE_CASE__		(			self			):
		with self.assertRaisesRegex(
		    snake_case						,       'bert-base is not a local folder and is not a valid model identifier'			):
			lowercase	    =       TFAutoModel.from_pretrained('bert-base'			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		with self.assertRaisesRegex(
		    snake_case						,       r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'			):
			lowercase	    =       TFAutoModel.from_pretrained(snake_case						,       revision='aaaaaa'			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		with self.assertRaisesRegex(
		    snake_case						,       'hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'						,       ):
			lowercase	    =       TFAutoModel.from_pretrained('hf-internal-testing/config-no-model'			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		with self.assertRaisesRegex(snake_case						,       'Use `from_pt=True` to load this model'			):
			lowercase	    =       TFAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'			)





	def       SCREAMING_SNAKE_CASE__		(			self			):
		# Make sure we have cached the model.
		lowercase	    =       TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert'			)
		with RequestCounter() as counter:
			lowercase	    =       TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert'			)
			self.assertEqual(counter.get_request_count						,       0			)
			self.assertEqual(counter.head_request_count						,       1			)
			self.assertEqual(counter.other_request_count						,       0			)

		# With a sharded checkpoint
		lowercase	    =       TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded'			)
		with RequestCounter() as counter:
			lowercase	    =       TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded'			)
			self.assertEqual(counter.get_request_count						,       0			)
			self.assertEqual(counter.head_request_count						,       1			)
			self.assertEqual(counter.other_request_count						,       0			)





"""simple docstring"""



from collections import Counter
from timeit import timeit
def      UpperCAmelCase     (       _lowercase :   str = ""			,   )						-> bool:


	"""simple docstring"""




	return sum(c % 2 for c in Counter(input_str.replace(''' '''			,   ''''''	).lower()	).values()	) < 2
def      UpperCAmelCase     (       _lowercase :   str = ""	)						-> bool:


	"""simple docstring"""




	if len(_lowercase	) == 0:
		return True
	lowerCAmelCase_      							=			input_str.replace(''' '''			,   ''''''	).lower()
	# character_freq_dict: Stores the frequency of every character in the input string
	lowerCAmelCase_      							=			{}

	for character in lower_case_input_str:
		lowerCAmelCase_      							=			character_freq_dict.get(_lowercase			,   0	) + 1
	lowerCAmelCase_      							=			0

	for character_count in character_freq_dict.values():
		if character_count % 2:
			odd_char += 1
	if odd_char > 1:
		return False
	return True
def      UpperCAmelCase     (       _lowercase :   str = ""	)						-> None:


	"""simple docstring"""




	print('''\nFor string = '''			,   _lowercase			,   ''':'''	)
	print(
	    '''> can_string_be_rearranged_as_palindrome_counter()'''			,   '''\tans ='''			,   can_string_be_rearranged_as_palindrome_counter(_lowercase	)			,   '''\ttime ='''			,   timeit(
	        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''			,   setup='''import __main__ as z'''			,   )			,   '''seconds'''			,   )
	print(
	    '''> can_string_be_rearranged_as_palindrome()'''			,   '''\tans ='''			,   can_string_be_rearranged_as_palindrome(_lowercase	)			,   '''\ttime ='''			,   timeit(
	        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''			,   setup='''import __main__ as z'''			,   )			,   '''seconds'''			,   )


if __name__ == "__main__":
		lowercase_							=							input(
		    'Enter string to determine if it can be rearranged as a palindrome or not: '
		).strip()
		benchmark(check_str)
		lowercase_							=							can_string_be_rearranged_as_palindrome_counter(check_str)
		print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")